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All Journal Journal of Computer System and Informatics (JoSYC) Jurnal Garuda Pengabdian Kepada Masyarakat
Khoiriah, Miftahul
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Penerapan Algoritma Support Vector Machine Untuk Mendeteksi Autisme Khoiriah, Miftahul; Kurniawan, Rakhmat
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5692

Abstract

Autism is a type of developmental disorder that can cause a neurological condition to disrupt brain function and impact a person's growth process, communication skills and social interaction abilities. In general, autism spectrum disorders can be detected in babies as early as 6 months. Things that interfere with a child's development occur because the structure of brain function is disturbed. This widespread disability is described as a spectrum disorder due to the considerable variation in how an individual manifests symptoms and their severity. By carrying out this detection, it can make it easier for parents to know whether their child has autism or not so they know what action to take. This research was conducted using a quantitative research methodology, where the research approach focuses on collecting and analyzing data that can be measured in numerical form using statistical techniques to obtain numbers and generalize. This approach involves the relationship between phenomena and cause and effect using a larger sample. After the previous stages are completed, then continue testing the prediction results using testing and accuracy data to obtain classification results. From the classification results above, the resulting classification value reaches 100% using test data and using accuracy values. Support Vector Machine (SVM) algorithm ) with a linear kernel has been applied to a dataset of autism in children. This model succeeded in separating classes well, showing that SVM is an effective algorithm for this classification problem.
Rancang Bangun Alat Pendeteksi Kematangan Buah Sawit Dengan Menggunakan Metode Image Processing Berdasarkan Komposisi Warna Syahira, Melani Alka; Khoiriah, Miftahul; Harahap, Rina Syafiddini
Jurnal Garuda Pengabdian Kepada Masyarakat Vol 1 No 2 (2023)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/gabdimas.v1i2.827

Abstract

Pemanfaatan citra digital sangat penting untuk mengetahui kematangan buah sawit dengan memanfaatkan sistem yang ada. Dengan adanya citra digital maka untuk menentukan kematangan buah sawit berdasarkan warnanya bisa dilakukan secara computing (berbasis teknologi), yaitu dengan menerapkan pengolahan citra menggunakan metode transformasi ruang warna HSV (Hue, Saturation, Value). Model warna HSV (Hue, Saturation, Value) mengelompokkan komponen intensitas dari informasi warna yang dibawa (hue dan saturation) dalam warna citra. Klasifikasi kematangan buah sawit dari pengujian 30 sampel citra buah sawit, dapat dilihat dari rentang nilai Hue. Ektrasi RGB ke HSV nilai pada kulit buah Sawit menghasilkan dua klasifikasi nilai rentang Hue, yaitu warna hitam kekuningan dengan nilai Hue (0.25604 - 0.59155) untuk sawit mentah, warna orange merah tua dengan nilai Hue (0.06511 - 0.12985) untuk sawit matang. Hasil dari deteksi kematangan dapat dilihat pada masing-masing pengujian dengan nilai presentase 100% untuk kategori buah sawit matang, 100% untuk kategori buah sawit mentah. Nilai presentase untuk pengujian keseluruhan data mempunyai presentase nilai yang baik dimana berpengaruh dalam mendeteksi kematangan sawit yaitu sebesar 100%. Maka dapat disimpulkan, bahwa pendeteksian kematangan buah sawit dapat dilakukan dengan menerapkan metode transformasi ruang warna HSV.
Co-Authors Harahap, Rina Syafiddini Rakhmat Kurniawan R Syahira, Melani Alka